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Publications

Publications by António Valente

2020

Autonomous Driving Car Competition

Authors
Alves, JP; Fonseca Ferreira, NMF; Valente, A; Soares, S; Filipe, V;

Publication
ROBOTICS IN EDUCATION: CURRENT RESEARCH AND INNOVATIONS

Abstract
This paper presents the construction of an autonomous robot to participating in the autonomous driving competition of the National Festival of Robotics in Portugal, which relies on an open platform requiring basic knowledge of robotics, like mechanics, control, computer vision and energy management. The projet is an excellent way for teaching robotics concepts to engineering students, once the platform endows students with an intuitive learning for current technologies, development and testing of new algorithms in the area of mobile robotics and also in generating good team-building.

2020

Motor Rehabilitation and Biotelemetry Data Acquisition with Kinect

Authors
Almeida de Araujo, FMA; Ferreira Viana Filho, PRF; Adad Filho, JA; Fonseca Ferreira, NMF; Valente, A; Soares, SFSP;

Publication
BIODEVICES: PROCEEDINGS OF THE 13TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES, VOL 1: BIODEVICES, 2020

Abstract
Accessibility and inclusiveness of people with disabilities is a recurring theme that is already perceived as an issue in the field of human rights. Ramps, elevators, among other devices aim at the inclusion of these individuals with limited mobility. Various types of motor limitations, specially partial limitations, are linked to corresponding physical-motor rehabilitation process, with the purpose of reducing or eliminating the patient's dependence on a caregiver or devices for adaptation. Patients with motor disabilities must practice physiotherapeutical exercises along a physician in order to perform body and muscle analysis to ensure the patient's well-being. To reach a more accurate analysis, physiotherapists use a range of devices to acquire patient data, such as the spirometer, to acquire the patient's breath intensity and lung capacity. Similarly, there are other technologies capable of acquiring motion data and quantifying them. This work aims to develop a system that, paired together with an exercise game project (exergame), can acquire and transmit the motion data acquired in-game for an easier and faster analysis of the patient's growth, relying on graphs, tables, and other visual indicators to improve the evaluation of physiotherapeutic treatments. The usage together with an exergame also has benefits such as increased patient compliance with the treatment and improvements in well-being.

2020

Chikungunya Virus Inhibitor Study based on Molecular Docking Experiments

Authors
Saraiva, AA; Jeferson, S; Miranda, C; Moura Sousa, JVM; Fonseca Ferreira, NMF; Batista Neto, JESB; Soares, S; Valente, A;

Publication
PROCEEDINGS OF THE 13TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES, VOL 3: BIOINFORMATICS

Abstract
Chikungunya virus disease transmitted by the sting of the mosquito 'Aedes aegypti' presenting an epidemic in some regions. In order to have an early diagnosis and the best treatment technique, it establishes the study of inhibitors for laboratory elaboration of a drug from molecular docking. As a result you have a better chance of using Suramin followed by Silibin.

2020

Classification of Optical Coherence Tomography using Convolutional Neural Networks

Authors
Saraiva, AA; Santos, DBS; Pedro, P; Moura Sousa, JVM; Fonseca Ferreira, NMF; Batista Neto, JESB; Soares, S; Valente, A;

Publication
PROCEEDINGS OF THE 13TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES, VOL 3: BIOINFORMATICS

Abstract
This article describes a classification model of optical coherence tomography images using convolution neural network. The dataset used was the Labeled Optical Coherence Tomography provided by (Kermany et al., 2018) with a total of 84495 images, with 4 classes: normal, drusen, diabetic macular edema and choroidal neovascularization. To evaluate the generalization capacity of the models k-fold cross-validation was used. The classification models were shown to be efficient, and as a result an average accuracy of 94.35% was obtained.

2020

Classification of Respiratory Sounds with Convolutional Neural Network

Authors
Saraiva, AA; Santos, DBS; Francisco, AA; Sousa, JVM; Ferreira, NMF; Soares, S; Valente, A;

Publication
PROCEEDINGS OF THE 13TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES, VOL 3: BIOINFORMATICS

Abstract
Noting recent advances in the field of image classification, where convolutional neural networks (CNNs) are used to classify images with high precision. This paper proposes a method of classifying breathing sounds using CNN, where it is trained and tested. To do this, a visual representation of each audio sample was made that allows identifying resources for classification, using the same techniques used to classify images with high precision.For this we used the technique known as Mel Frequency Cepstral Coefficients (MFCCs). For each audio file in the dataset, we extracted resources with MFCC which means we have an image representation for each audio sample. The method proposed in this article obtained results above 74%, in the classification of respiratory sounds used in the four classes available in the database used (Normal, crackles, wheezes, Both).

2019

Development of help and surveillance technologies for dependent elderly people at home

Authors
Rodrigues, V; Monteiro, MJ; Soares, S; Valente, A; Silva, S; Sousa, M; Duarte, D; Rainho, C; Barroso, I;

Publication
EUROPEAN JOURNAL OF PUBLIC HEALTH

Abstract

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